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This report contains paper abstracts of the workshop "Visualization and Mathematics" held in Berlin-Dahlem in September 1997. The meeting serves as a forum for an international community of researchers and practitioners on the application of visualization techniques in mathematics and on mathematical concepts in visualization. It is the second symposium in a series of workshops bringing together mathematicians and experts from scientific visualization. The themes of the workshop include: \begin{itemize} \item - applications in differential geometry and partial differential equations \item - algorithmic aspects of adaptive and hierarchical techniques in space and time \item - time control of animated objects and corresponding algorithms \item - algorithmic representation of objects for display, storage and exchange \item - new visualization techniques for mathematical structures \item - integration of visualization with symbolic and numerical computation. \end{itemize}
We provide conditions for convergence of polyhedral surfaces and their discrete geometric properties to smooth surfaces embedded in Euclidian $3$-space. The notion of totally normal convergence is shown to be equivalent to the convergence of either one of the following: surface area, intrinsic metric, and Laplace-Beltrami operators. We further s how that totally normal convergence implies convergence results for shortest geodesics, mean curvature, and solutions to the Dirichlet problem. This work provides the justification for a discrete theory of differential geometric operators defined on polyhedral surfaces based on a variational formulation.
We present a new algorithm for fairing of space curves with respect spatial constraints based on a vector valued curvature function. Smoothing with the vector valued curvature function is superior to standard Frenet techniques since the individual scalar components can be modeled similar to curvature-based curve smoothing techniques in 2d. This paper describes a curve smoothing flow that satisfies strict spatial constraints and allows simultaneous control of both curvature functions.
In high accuracy numerical simulations and optimal control of time-dependent processes, often both many time steps and fine spatial discretizations are needed. Adjoint gradient computation, or post-processing of simulation results, requires the storage of the solution trajectories over the whole time, if necessary together with the adaptively refined spatial grids. In this paper we discuss various techniques to reduce the memory requirements, focusing first on the storage of the solution data, which typically are double precision floating point values. We highlight advantages and disadvantages of the different approaches. Moreover, we present an algorithm for the efficient storage of adaptively refined, hierarchic grids, and the integration with the compressed storage of solution data.
MathInsight 2002
(2001)
Visualization
(2014)